Asia-Pacific
Four steps in unlocking
the value of data in
Australian industrial
organisations
Data hold a wealth of potential value for industrial organisations, but
accessing that value can be more difficult than it may first appear.
This article was a collaborative effort by Cris Cunha, Tim Fountaine, Alejandro Rosales, Marcus Roth,
and Christine Savage.
© Cravetiger/Getty Images
May 2021
Australian industrial organisations recognise
data as a critical asset, but many are struggling to
convert that potential to benefit their bottom line.
Faced with the dual obstacle of changing choices
and complex legacy systems, organisations fail to
take action or else embrace piecemeal investment
in technologies without clear direction.
In this short insight, we look at how strategy, culture,
and capability retooling set data leaders in this
sector apart—and outline the steps needed to join
them.
Australian industry provides some exemplars of how
accelerating technology capabilities can capture
value. In mining, for instance, the value gained
from deploying advanced analytics at scale is clear,
especially in the optimisation of plant processes or
in predictive maintenance.
Because data are the fuel behind these
opportunities, other industrial players are seeking
similar capabilities. This has prompted acceleration
in building data platforms, remediating source
systems, and streamlining architectures.
Yet the sectorwide results are mixed. Despite
strong efforts, most industrial organisations are
falling short of capturing full value from their data—
and they know it. In one survey, only 7 percent of
Australian organisations rated themselves as ‘very
effective’ at reaching their primary objectives in data
and analytics, putting the ROI of these efforts in
question (Exhibit 1).
If this sounds familiar, your business is not alone. It
isn’t surprising when we consider the simultaneous
challenges industrial businesses face in delivering
real impact from data.
In the face of this, is it any wonder that multitudes of
hastily deployed ‘pilots’—loosely aligned (if at all) to
governance and value orientation—can become the
norm (Exhibit 2)?
This needn’t be the case.
Exhibit 1
Many organisations
organisations are
are falling
falling short
short of
of their
their goals
goalsof
ofobtaining
obtainingvalue
valuefrom
fromdata.
data.
7%
48%
72%
Only 7% of organisations recently
surveyed said they are ‘very effective’
at reaching their primary objectives on
data and analytics.¹
A staggering 48% said they are
neutral or ineffective.¹
Among leading organisations across
industries, 72% noted managing data
as being among the top challenges
preventing them from scaling data
and analytics impact.²
Managing data and its supporting technology is one of the top barriers organisations are facing.
Value quantification is key to prioritising activities.
1
2
2
McKinsey Quarterly survey in March 2017: ‘How effective has your organisation been at reaching the primary objective of its data and analytics activities?’
Survey of members invited to McKinsey’s Advanced Data and Analytics Roundtable in November 2017.
Four steps in unlocking the value of data in Australian industrial organisations
Exhibit 2
A wide range of issues can prevent organisations from properly
A
wide range
of issues can prevent organisations from properly harnessing data.
harnessing
data.
Large upfront investments
with unclear returns
There is limited focus on early value demonstration, either through
removing costs or launching quick wins; the business doesn’t see the point
in continuing investment.
Plans are not ‘fit for purpose’
Use cases are chosen based on industry trends instead of aligning
carefully with business goals; data and analytics applications
become theoretical.
Data governance is just
theoretical
Data-governance standards are loosened to ‘enable’ the data lake
but unfortunately lead to the creation of a data swamp without
proper standards.
Operating model is not
well defined
Misaligned incentives and confusion around roles and between
groups yield uncoordinated efforts: IT, data, and analytics may be
completely independent.
Small renegade groups already operating ad hoc with new technologies
(for example, cloud) make it difficult to adopt those technologies at scale.
Dependence on silo-driven
technology
Focusing too much on the technology instead of on capability building and
adoption yields cutting-edge tech that nobody uses.
Heavy reliance on existing large vendor contracts increases the up-front
cost and risk level for a transformation.
Large number of pilots
across the business
Many parts of the business trying different concepts (self-service business
intelligence platforms, data streaming, different levels of governance) with
low or no coordination.
Developing and obtaining up-front buy-in to a
data ‘blueprint’ can underpin a data-investment
approach that is strategic, measured, managed,
and engineered to your organisation’s core
priorities and products.
Developing the blueprint is not a simple task, but it’s
a worthwhile one. If done right, it will balance value
delivery and long-term capability building through:
— A value-back data strategy. Start with a
compelling vision—with executive buy-in—of
how data and analytics will propel broader
business strategy. This strategy must include an
agreed-on business case and a two- to threeyear road map of prioritised opportunities and
their enabling capabilities.
— A fit-for-purpose technology-infrastructure map.
Modernising data architecture should not be a
multiyear effort in which value is realised only
at the end. A progressive approach should be
adopted, with high-quality, ready-to-consume
data becoming available over time in the format
needed by your business. Reusability is a key for
acceleration: often, a handful of data areas will
enable most of the highest-value opportunities.
— A robust data-governance model. Roles,
processes, and tools to address data ownership,
quality, security, access, and ethics (the ‘safety’
of the data world) must be put in place—and
centrally understood. These elements may be
rolled out progressively, focusing on those that
enable the highest value first, but the full model
should be clear up front.
Four steps in unlocking the value of data in Australian industrial organisations
3
— A data-driven leadership culture. Building
this culture requires understanding existing
mindsets about data and, often, intervening.
Many organisations still consider data to be
an IT problem instead of a subject that should
be integrated at senior levels. As in most
transformations, culture is the hardest element
to influence, requiring a mix of approaches,
including role modeling, incentive alignment,
and comprehensive change management and
communication.
— A deep-skilled, data-literate workforce. Securing
tech skills is critical but not sufficient.
Many organisations have gaps in broad data literacy,
reducing the potential for data-driven decision
making and creating ineffective ‘internal clients’ for
data stewards. Lead organisations are addressing
these gaps by rolling out ‘data academies,’ with
online training and informal learning tailored to
existing roles and contexts.
Stop stalling. Start drafting!
Combining all the above factors can be daunting and
often causes industrial (and other) organisations to
stall and fail to take action.
So here are the four steps industrial organisations
can take right away to avoid the ‘pilot purgatory’ trap.
Step 1: Identify the data you most need
and the way you most need it
All data are important, but not all data are equal—nor
are they consumed in the same way.
Examine your value chain and identify the points
with the greatest potential for improvement from
analytics, automation, digitisation, and so on—and
map those opportunities to the data domains
required to enable them. These are your priority
data domains (Exhibit 3).
Then identify how those data will be most useful
to drive your technology. For example, plant
optimisation may require ingesting data as rapidstream computing parameters and passing these
Exhibit 3
Identifying priority
priority data
data domains is key
key in value creation.
creation.
Illustrative
Data domain needed
Use cases
Value
Equipment
Plant operations
Maintenance
Site operations
Geology
Inventory
Predictive maintenance
for trucks
Truck-loading optimisation
Plant-processing optimisation
Machine learning for
geological modelling
Inventory-management
optimisation
People analytics for retention
Business-to-business
sales optimisation
Source: McKinsey analysis
4
Four steps in unlocking the value of data in Australian industrial organisations
Human resources
Sales
Market data
parameters to machines. The technical ability to
deliver the data consumption your business needs
should be prioritised over the value it can create.
Step 2: Empower a small team of
high-performing experts focused
on delivering one or two high-value
opportunities
Data teams are usually structured in one of two ways:
1. Large, centralised teams, working on ingesting
and transforming data—with requirements
‘thrown over the fence’ by business, operations,
or analytics teams. These teams are good at
adhering to processes and standards but tend to
be slow and expensive.
2. Small ‘pirate crews’ formed by staff frustrated
with a lack of easy access to high-quality, readyto-use data. They find ways to extract raw data,
save them in private repositories, and manipulate
them based on their needs to generate insights
(which, in some cases, are inconsistent with
reports from other parts of the business).
We believe the sweet spot lies somewhere in the
middle. We see leading organisations adopting agile
delivery models in which ‘use squads,’ focused on
delivering end-user functionality (for example, a
truckload optimisation solution), work with ‘utility
squads,’ focused on ensuring the required data
foundations are in place (Exhibit 4).
The advantage of bringing these groups together is
that both tend to:
— be highly integrated with the business
— have a backlog prioritised by value
— adopt a product mindset (rather than a
project mindset)
— rely on automation to reduce lead time
Exhibit 4
Organisations
different
Organisations should
should adopt
adoptagile
agiledelivery
deliverymodels
modelsthat
thatemploy
employtwo
two
different
types of
squads.
of squads.
Use-case squads
Squad 3 (Use case 1)
Squad 2 (Use case 2)
Squad 1 (Use case 3)
Data platform
Data-utility squads
Squad 3 (Data domain 1)
Squad 2 (Data domain 2)
Squad 1 (Data domain 3)
Source systems
Four steps in unlocking the value of data in Australian industrial organisations
5
Organisations that go down this path start small
with a core team focused on delivering one or two
use cases with clear links to value. Early wins create
excitement in your teams and help identify lessons
that can be used to scale as demand grows.
Step 3: Selectively modernise your
data architecture, leveraging new
approaches to scale up
Some organisations are pivoting from centralenterprise data to a domain-led architecture,
leading to improved time-to-market on data-driven
services and products.
This requires up-front effort to design the
architectural capabilities for each data
domain—but in the long run, it reduces the risk
of fragmentation and inefficiency and simplifies
the construction of data models, accelerating the
enablement of data services.
Domain-specific architectural elements can be
deployed and replicated easily by leveraging
‘infrastructure as code,’ which allows for rapid scaling
both within and across data domains (Exhibit 5).
Organisations adopting this approach start by
focusing on two or three high-priority data domains
and the associated data-consumption archetypes.
Step 4: Repeat and scale
Successful organisations progressively build the
muscle needed to use data effectively, approaching
it as a continuous improvement journey. A critical
success factor is ensuring that the approach is
codified and repeatable.
The first set of use cases is extremely important
for standardising the approach and helping build
momentum in your organisation. These early wins
attract more internal buy-in and avoid the dreaded
‘technology-first’ mindset.
Being data-driven and digitally enabled can now
make or break industrial businesses.
Case study: Global mining organisation
A leading Australian mining
organisation had hundreds of siloed
operational databases scattered across
multiple sites and geographies around
the world. Every new analytics use case
or digital application required months of
data discovery, ingestion, and cleansing,
with little to no documentation and a lack
of common standards, resulting in more
than 200 proof-of-concepts but very little
value demonstrated.
6
The organisation launched an integrated
technology-modernisation program,
including a shift from an on-premise
to a cloud-centric approach, with a
data-operating model underpinned by
a standardised global data architecture
and business-unit-specific components.
Rather than building a ‘big bang’ platform
approach, the organisation created cloudnative data pipelines to immediately link
high-value use cases to a data domain—
Four steps in unlocking the value of data in Australian industrial organisations
and, in doing so, unlocked all data from
source systems to be reliable, in the right
format, and accessible.
This approach drastically reduced the
time spent on data-engineering activities,
achieving sevenfold acceleration on the
delivery of use cases while also increasing
the stability and reusability of data.
‘Sweating’ the value of your data requires a staged
approach, balancing high-value opportunities with
long-term capabilities, including a data-literate
workforce and a flexible architecture that supports
your organisation’s objectives.
While we know (and have seen firsthand) the
challenges in achieving this, we believe that a
‘value back’ data blueprint, paired with a rigorous
execution anchored in agile delivery, gives industrial
businesses the best chance of data success.
Exhibit 5
Deploying domain-specific
allows
forfor
rapid
scaling
domain-specificarchitectural
architecturalelements
elements
allows
rapid
scaling
within and
and across
across data
within
datadomains.
domains.
Understand needs and upgrade your data architecture to achieve a ‘value back’ blueprint
Data
What data
do I need?
Cloud-native
infra-as-code
components
Which technology
components are
needed?
Architectural
patterns
How should the
components be
combined?
Consumption
archetypes
What are the
data-access needs?
Use cases
What use cases do
I need to deliver?
$XX
Cris Cunha, based in Perth, is a partner at QuantumBlack, a McKinsey company; Tim Fountaine, based in Sydney, is a senior
partner at QuantumBlack; Alejandro Rosales is a consultant in McKinsey’s Sydney office; Marcus Roth is a partner in the
Tokyo office; and Christine Savage is a consultant in the Perth office.
Copyright © 2021 McKinsey & Company. All rights reserved.
Four steps in unlocking the value of data in Australian industrial organisations
7